AIMC Topic: Quantitative Structure-Activity Relationship
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Evaluation of antiarrhythmia drug through QSPR modeling and multi criteria decision analysis.
Scientific reports
Aug 9, 2025
This study explores how topological indices (TIs), which are mathematical descriptors of a drug's molecular structure, can support to predict vital properties and biological activities. This understanding is a key for more effective drug design. We f...
Structure-based virtual screening, molecular docking, and MD simulation studies: An in-silico approach for identifying potential MBL inhibitors.
PloS one
Jul 31, 2025
The global rise of antibiotic-resistant infections has been driven in part by the spread of bacteria producing metallo-β-lactamase (MBL), particularly New Delhi metallo-β-lactamase-1 (NDM-1). Currently, there are no clinically approved inhibitors tar...
Prediction of Fraction Unbound in Human Plasma for Per- and Polyfluoroalkyl Substances: Evaluating Transfer Learning as an Algorithmic Solution to the Problem of Sparse Data.
Journal of chemical information and modeling
Jul 22, 2025
Fraction unbound in plasma () is a crucial parameter in physiologically based toxicokinetic (PBTK) models, representing the fraction of a chemical compound that is not sequestered by plasma proteins when present in the bloodstream. This is often used...
Advanced QSPR modeling of profens using machine learning and molecular descriptors for NSAID analysis.
Scientific reports
Jul 20, 2025
In this paper, we present a predictive model based on artificial neural network (ANN) to evaluate principal physicochemical properties of a set of anti-inflammatory drugs based on chosen topological indices. The molecular descriptors were calculated ...
ML enhanced bioactivity prediction for angiotensin II receptor: A potential anti-hypertensive drug target.
Scientific reports
Jul 14, 2025
The process of drug discovery is intricate, and encompasses a series of detailed phases of research, development, and testing, aimed at evaluating the safety and effectiveness of prospective therapeutic agents. Artificial Intelligence has emerged as ...
Exploring entropy measures with topological indices on colorectal cancer drugs using curvilinear regression analysis and machine learning approaches.
PloS one
Jul 9, 2025
A topological index is a numerical value derived from the structure of a molecule or graph that provides useful information about the molecule's physical, chemical, or biological properties. These indices are especially important in chemo-informatics...
Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.
Journal of computer-aided molecular design
Jul 8, 2025
Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors tha...
A computational study of cardiac glycosides from Vernonia amygdalina as PI3K inhibitors for targeting HER2 positive breast cancer.
Journal of computer-aided molecular design
Jul 6, 2025
The PI3K/Akt pathway plays a crucial role in regulating a broad network of proteins involved in the proliferation of HER2-positive breast cancer. The ethyl acetate fraction of Vernonia amygdalina, which contains cardiac glycosides, has been shown to ...
Quantitative Structure-Activity Relationship
Female
Protein Kinase Inhibitors
Humans
Molecular Dynamics Simulation
Breast Neoplasms
Phosphoinositide-3 Kinase Inhibitors
Machine Learning
Receptor, ErbB-2
Molecular Docking Simulation
Class I Phosphatidylinositol 3-Kinases
Phosphatidylinositol 3-Kinases
Cardiac Glycosides
QSAR Model Development for the Environmental Risk Limits and High-Risk List Identification of Phenylurea Herbicides in Aquatic Environments.
Journal of agricultural and food chemistry
Jun 9, 2025
Due to the extensive residues of phenylurea herbicides (PUHs) in the environment, it is important for the ecological risk assessment of PUHs to determine their environmental risk limits and identify the high-risk PUHs. This study derived the environm...
Machine Learning Based Quantitative Structure-Dissolution Profile Relationship.
Journal of chemical information and modeling
Jun 5, 2025
Determining accurate drug dissolution processes in the gastrointestinal tract is critical in drug discovery as dissolution profiles provide essential information for estimating the bioavailability of orally administered drugs. While various methods h...
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